Newly trained lexical categories produce lateralized categorical perception of color Ke Zhou a,1 , Lei Mo b,1 , Paul Kay c,d,2 , Veronica P. Y. Kwok e,f , Tiffany N. M. Ip e,f , and Li Hai Tan e,f,2 a State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; b Center for the Study of Applied Psychology, South China Normal University, Guangzhou 510631, China; c Department of Linguistics, University of California, Berkeley, CA 94720; d International Computer Science Institute, Berkeley, CA 94704; and e State Key Laboratory of Brain and Cognitive Sciences and f Department of Linguistics, University of Hong Kong, Hong Kong Contributed by Paul Kay, April 25, 2010 (sent for review March 31, 2010) Linguistic categories have been shown to in?uence perceptual discrimination, to do so preferentially in the right visual ?eld, to fail to do so when competing demands are made on verbal memory, and to vary with the color-term boundaries of different languages. However, because there are strong commonalities across languages in the placement of color-term boundaries, the question remains open whether observed categorical perception for color can be entirely a result of learned categories or may rely to some degree on innate ones. We show here that lateralized color categorical perception can be entirely the result of learned categories. In a visual search task, reaction times to targets were faster in the right than the left visual ?eld when the target and distractor colors, initially sharing the same linguistic term (e.g., “blue”), became between-category colors after training (i.e., when two different shades of blue had each acquired a new name). A control group, whose conditions exactly matched those of the experimental group except that no new categories were intro- duced, did not show this effect, establishing that the effect was not dependent on increased familiarity with either the color stim- uli or the task. The present results show beyond question that lateralized categorical perception of color can re?ect strictly learned color categories, even arti?cially learned categories that violate both universal tendencies in color naming and the catego- rization pattern of the language of the subject. category learning | Whorf hypothesis | nature versus nurture | linguistic relativity A long-standing “Whor?an” debate over the relation between language andthought has gained momentum in recent years with an increasing number of studies demonstrating the in- volvement of linguistic information in categorical perception of color (1–18).* For example, speakers of English judge colors that straddle the English category boundary between green and blue to be less similar than do speakers of Tarahumara, a Uto- Aztecan language of Mexico that uses a single word for these colors (1). Unlike English, Russian makes a distinction between lighter blues (goluboy) and darker blues (siniy), and Russian speakers are faster, compared with English speakers, in discrim- inating two colors when they fall into different categories, one goluboy and the other siniy, than when they belong to the same category(6).Morerecent?ndingsprovideadifferentperspective, suggesting that language is disproportionately engaged in the discrimination of colors presented in the right visual ?eld (RVF) as compared with the left visual ?eld (LVF) (5, 7, 8, 10, 11). Speci?cally, discrimination of colors from two different lexical categories(e.g.,agreenamongblues)isfasterthandiscrimination of colors from the same lexical category (e.g., one green among tokensofadifferentgreen),butonly(orpredominantly)whenthe between-category colors are presented in the RVF. [Signi?cant color categorical perception (CP) has also been found in the LFV (7, 8) but it is always weaker than in the RVF. Because it is as- sociated with longer response times, this effect has been argued to result from transcallosal transfer or in some cases scanning (7, 14, 18).] This RVF advantage arises from the activation of lan- guage regions of the left cerebral hemisphere (13, 14), to which the RVF projects, where the lexical distinction of colors may exaggerate or reduce the perceptual difference (19, 20). In sum, there is a lateralized Whorf effect: linguistic categories ?lter some, but not all, perceived inputs (16–18). Earlier results have shown that lateralized color CP varies according to the location of category boundaries in individual languages (5–8) and disappears when verbal interference, but not comparable nonverbal interference, is induced by a concurrent task (5, 6). On the other hand, color-term boundaries tend to be similar across languages (21–26), and prelinguistic infants and toddlers display CP at color-term boundaries that accord well with universal tendencies in color naming (10, 11, 27). Moreover, most of the color CP experiments have concentrated exclusively or largely on the blue/green boundary, present of course in En- glish, but also widespread in the world. Therefore, there is un- certainty regarding the degree to which color CP, as so far observed, re?ects learned versus presumably innate categories, especially because linguistic and seemingly innate color catego- ries tend to coincide. It has been shown that perceptual dis- crimination can be trained to new category distinctions that will then produce observable CP effects (19, 28, 29). ?zgen and Davies have shown speci?cally that category effects can be trained in the color domain, although they did not train explicitly verbal categories (30). If a lateralized category effect can be shown to occur for experimentally trained verbal categories, it will follow that the lateralized Whorf effect can occur even when the categories involved violate both universal tendencies in color naming and those of the language of the subjects. Such a ?nding will reinforce the behavioral evidence reviewed above, as well as the corroborating event-related potential (ERPs) and fMRI ev- idence (13, 14, 31), that color CP, lateralized to the left hemi- sphere and suppressible by verbal interference, is a language- dependent (“Whor?an”) phenomenon. Using an intensive training method to teach subjects new linguistic terms for colors originally from the same lexical cate- Author contributions: K.Z., L.M., P.K., V.P.Y.K., T.N.M.I., and L.H.T. designed research; K.Z., L.M., and V.P.Y.K. performed research; K.Z. analyzed data; and K.Z., L.M., P.K., V.P.Y.K., T.N.M.I., and L.H.T. wrote the paper. The authors declare no con?ict of interest. 1 K.Z. and L.M. contributed equally to this work. 2 To whom correspondence may be addressed. E-mail: [email protected] or [email protected] hku.hk. *We follow common usage in employing the expression “categorical perception” to de- note the ?ndings of the experiments cited here that establish category effects in color discrimination. We do not thereby assume a position on whether these effects will ultimately be found to lie on the perception or the response side. In particular, several of the studies cited below as establishing categorical perception demonstrate category effects against a baseline measure that is, at least nominally, equally spaced perceptu- ally, and which would therefore already be adjusted for categorical perception effects if it is indeed perceptually equally spaced in the task setting. However, the present study, like some prior studies (4, 5, 7, 13, 14), is not dependent on the spacing of the stimuli because the differential effects in the two visual ?elds (or two cerebral hemispheres) demonstrate that whatever the “true” spacing of the stimuli may be, the RFV/LH is more sensitive to category difference than the LVF/RH in speaking adults. 9974–9978 | PNAS | June 1, 2010 | vol. 107 | no. 22 www.pnas.org/cgi/doi/10.1073/pnas.1005669107 gory, we demonstrate such a de?nite cause-effect relationship. A version of the visual search task employed in the original later- alized Whorf study (5) was administered to 31 adults divided into two groups, closely matched for age and sex. Each stimulus display included colors selected from a set of four (Fig. 1 A and B), forming a ring of colored squares surrounding a central ?x- ation point (Fig. 1C). All of the squares were of the same color except the target. Following central ?xation, participants were shown the ring of colors and asked to indicate whether the target was on the left (LVF) or right (RVF) side of the circle, by making a speeded button-press response with the corre- sponding hand. Group 1 (experimental group, n = 18 Mandarin Chinese speakers) performed the visual search task twice, once before and once after training. The four colors, which we designate green 1 (G1), green 2 (G2), blue 1 (B1), and blue 2 (B2), form a graded series from green to blue. The boundary between4ó “green” andX\ “blue” falls between G2 and B1 (Fig. 1A). Before the training session, the target and distractor colors were either from the same lexical category (i.e., B1B2 or G1G2; “within category”) or from distinct lexical categories (i.e., G2B1; “be- tween category”). In this manner, two variables were manipu- lated in the before-training search task: the categorical re- lationship between the target and distractor colors (between vs. within category) and the visual ?eld of the target (LVF vs. RVF) (Fig. 1C). After completion of the ?rst visual search task, group 1 received intensive training in assigning colors G1, G2, B1, and B2, respectively, to four new lexical categories, named with meaningless monosyllables: áng, sòng, duC22an, and kC20en. Thus, the within-category colors before training became between-category colors after training, although the colors themselves did not change. The training involved six individual sessions (total train- ing time of 3 h), spread over 3 days, and included three activities: listening, naming, and matching. For the listening task, subjects simply heard each new word while viewing the appropriate color. In the naming task, while a color was displayed the subject was required to give the new color name; immediate feedback was provided. The matching task required subjects to decide whether the sound they heard was the new name for the color viewed on the computer screen. Again, immediate feedback was given. Participants successfully learned the new categories within the training period, as discussed below and summarized in Fig. 2. After the training period, subjects performed the visual search task again (Fig. 1C), but now every target color belonged to a different lexical category from the accompanying distractors. This training task is different from the perceptual learning par- adigm of categorical color perception (28–30) in that we ex- plicitly assigned a new linguistic term to each of the four stimulus colors: A, B, C, and D. Our interest is focused on subjects’ responses to the color pairs that had been within-category before training but became between-category after training. The goal was to determine whether pairs of colors that were within- category before training would be discriminated faster after training had assigned them to distinct linguistic categories, and if so whether the difference in speed of discrimination would be greater in the RVF than the LVF, revealing a lateralized Whorf effect on newly learned categories. To exclude the possibility that any observed difference be- tween pre- and posttraining trials might be the result of a gain in familiarity with the stimuli or task demands, we included group 2 (a control group, n = 13), who also performed a second visual search task, but whose intersearch-task experience consisted simply in performing the identical tasks that the experimental group did in learning the new color words using only the original Mandarin color terms (i.e.,4ó “green” andX\ “blue”). This practice regime simply gave subjects redundant exposure to color terms they already knew. (For the reader’s convenience, we use the word “training” in connection with the experimental group and “practice” in connection with the control group.) Results and Discussion Experimental Group. Trials in which the participant pressed the wrong key or in which the reaction time (RT) was 2 SD from the grand mean were excluded. Response accuracies were very high (94% in all conditions) and did not show any statistically signi?cant differences between conditions, so our analysis fo- cused on RTs, as illustrated in Fig. 3. We ?rst veri?ed that there was a signi?cant lateralized Whorf effect in color search before training. Using a 2 (visual ?eld: LVF vs. RVF) × 2 (category type: within- vs. between-category) ANOVA (Fig. 3A), we found a highly reliable category effect, with between-category RTs faster than within-category RTs (455 ms vs. 476 ms), F(1,17) = 38.13, P 0.5]. Critically, we compared RTs for within-category vs. between- categoryconditionswithineachvisual?eld.ForRVFtargets,RTs inthebetween-categorycondition(447ms)were31msfasterthan inthewithin-categorycondition[447msvs.478ms;t(17)=?4.98, P 0.2.Importantly,therewasasigni?cant training × visual ?eld in- teraction,F(1,17)=6.58,P0.02,resultingfromthefactthatRVF posttraining between-category RTs were the shortest. Although there had been no reliable difference between the RVF and the LVF for within-category pairs before training (as described above: 478 ms vs. 474 ms), following training RTs were signi?cantly faster inthe RVF than inthe LVF [430msvs. 442 ms, t(17) =?3.01, P 0.009]; that is, after these pairs had become between-category. For RVF targets, RTs in the posttraining between-category condition were 48 msfasterthan in the pretraining within-category condition (430msvs.478ms),t(17)=?5.20,P0.001.ForLVFtargets,RTs in the between-category condition (posttraining) were 32 ms faster than in the within-category condition (pretraining), t(17) = ?3.89, P 0.002. Still, there was a signi?cant lateralized Whor?an effect (48 ms vs. 32 ms), t = 2.56, P 0.02. To further examine whether there was a difference in category effect because of training in the two visual ?elds, we also com- pared RTs for the colors that were between-category both before and after training (Fig. 3C). Overall, training resulted in faster RTs, F(1,17) = 10.01, P 0.006, but there were RVF advantages both before [447 ms vs. 463 ms, t(17) = ?3.11, P 0.007] and after training [418 ms vs. 435 ms, t(17) = ?3.69, P 0.003]. The effect size (16 ms before training and 17 ms after training) was not modulated by training, as indicated by a nonsigni?cant visual ?eld × training interaction (F 1). Control Group. To rule out the possibility that the claimed Whor?an effect after training in the experimental group might stem from increased familiarity with either the stimuli or task demands, we tested 13 subjects in a control group. These subjects performed the search task twice but, in the place of training on new categories, they underwent a practice session th